Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes
The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex ba...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/823541 |
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| author | Maya Eapen Reeba Korah G. Geetha |
| author_facet | Maya Eapen Reeba Korah G. Geetha |
| author_sort | Maya Eapen |
| collection | DOAJ |
| description | The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-d5e8ea0f6fa64d39bddf78592d374e19 |
| institution | OA Journals |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-d5e8ea0f6fa64d39bddf78592d374e192025-08-20T02:19:40ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/823541823541Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT VolumesMaya Eapen0Reeba Korah1G. Geetha2Department of Computer Science and Engineering, Jerusalem College of Engineering, Chennai 600100, IndiaDepartment of Electronics and Communication Engineering, Alliance University, Bangalore 562106, IndiaDepartment of Computer Science and Engineering, Jerusalem College of Engineering, Chennai 600100, IndiaThe segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method.http://dx.doi.org/10.1155/2015/823541 |
| spellingShingle | Maya Eapen Reeba Korah G. Geetha Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes The Scientific World Journal |
| title | Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes |
| title_full | Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes |
| title_fullStr | Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes |
| title_full_unstemmed | Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes |
| title_short | Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes |
| title_sort | swarm intelligence integrated graph cut for liver segmentation from 3d ct volumes |
| url | http://dx.doi.org/10.1155/2015/823541 |
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